Siobhán Stynes
Clinical diagnostic model for sciatica developed in primary care patients with low back-related leg pain
Stynes, Siobhán; Konstantinou, Kika; Ogollah, Reuben O.; Hay, Elaine M.; Dunn, Kate M.
Authors
Kika Konstantinou
REUBEN OGOLLAH REUBEN.OGOLLAH@NOTTINGHAM.AC.UK
Associate Professor of Medical Statistics and Clinical Trials
Elaine M. Hay
Kate M. Dunn
Abstract
Background
Identification of sciatica may assist timely management but can be challenging in clinical practice. Diagnostic models to identify sciatica have mainly been developed in secondary care settings with conflicting reference standard selection. This study explores the challenges of reference standard selection and aims to ascertain which combination of clinical assessment items best identify sciatica in people seeking primary healthcare.
Methods
Data on 394 low back-related leg pain consulters were analysed. Potential sciatica indicators were seven clinical assessment items. Two reference standards were used: (i) high confidence sciatica clinical diagnosis; (ii) high confidence sciatica clinical diagnosis with confirmatory magnetic resonance imaging findings. Multivariable logistic regression models were produced for both reference standards. A tool predicting sciatica diagnosis in low back-related leg pain was derived. Latent class modelling explored the validity of the reference standard.
Results
Model (i) retained five items; model (ii) retained six items. Four items remained in both models: below knee pain, leg pain worse than back pain, positive neural tension tests and neurological deficit. Model (i) was well calibrated (p = 0.18), discrimination was area under the receiver operating characteristic curve (AUC) 0.95 (95% CI 0.93, 0.98). Model (ii) showed good discrimination (AUC 0.82; 0.78, 0.86) but poor calibration (p = 0.004). Bootstrapping revealed minimal overfitting in both models. Agreement between the two latent classes and clinical diagnosis groups defined by model (i) was substantial, and fair for model (ii).
Conclusion
Four clinical assessment items were common in both reference standard definitions of sciatica. A simple scoring tool for identifying sciatica was developed. These criteria could be used clinically and in research to improve accuracy of identification of this subgroup of back pain patients.
Citation
Stynes, S., Konstantinou, K., Ogollah, R. O., Hay, E. M., & Dunn, K. M. (2018). Clinical diagnostic model for sciatica developed in primary care patients with low back-related leg pain. PLoS ONE, 13(4), Article e0191852. https://doi.org/10.1371/journal.pone.0191852
Journal Article Type | Article |
---|---|
Acceptance Date | Jan 12, 2018 |
Publication Date | Apr 5, 2018 |
Deposit Date | Apr 12, 2018 |
Publicly Available Date | Apr 12, 2018 |
Journal | PLoS ONE |
Electronic ISSN | 1932-6203 |
Publisher | Public Library of Science |
Peer Reviewed | Peer Reviewed |
Volume | 13 |
Issue | 4 |
Article Number | e0191852 |
DOI | https://doi.org/10.1371/journal.pone.0191852 |
Public URL | https://nottingham-repository.worktribe.com/output/923775 |
Publisher URL | http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0191852 |
Contract Date | Apr 12, 2018 |
Files
Stynes et al 2018- Plos One.pdf
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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0
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